library(SuperCell)
library(Seurat)
#library(zellkonverter)
library(SingleCellExperiment)
library(ggplot2)
library(harmony)
library(reshape2)
In this tutorial we will make a short reanalysis of a large single cell transcriptome atlas of COVID19 at the metacell level. The orignal study gathered 1.4 millions of cells distributed in 284 samples coming from 196 patients. Here we will focus on a subset of 26 fresh PBMC samples.
We start by analyzing a fresh PBMC sample from an aged patient that deceased from a severe COVID 19. We will mainly consider the fist cell annotation to major PBMC cell types coming from the original study (‘majorType’ column in the metadata).
As in the original study we will discard immunoglobuline, ribosomal protein and mitochondrial genes for the dimension reduction analysis and the metacell construction.
These gene lists of genes were retrieved from the genenames website. In this tutorial we provide you the whole gene blacklist as an R object.
gene_blacklist <- readRDS("../data/gene_blacklist.rds")
Quality control (gene/cell filtering) was already done in the original study. We go directly to dimension reduction on highly variable genes
pbmc <- readRDS("../data/pbmc_COVID19_sce/S-S086-2_sce.rds")
sceRawToSeurat<- function(pbmc,nfeatures = 1500) {
pbmc <- CreateSeuratObject(counts = assay(pbmc),meta.data = data.frame(colData(pbmc)))
pbmc <- FindVariableFeatures(pbmc,nfeatures = nfeatures)
VariableFeatures(pbmc) <- VariableFeatures(pbmc)[!VariableFeatures(pbmc) %in% gene_blacklist]
pbmc <- NormalizeData(pbmc)
}
pbmc <- sceRawToSeurat(pbmc)
First we can do a Principal Component Analysis (PCA) of this sample with the Seurat package.
pbmc <- ScaleData(pbmc)
pbmc <- RunPCA(pbmc, npcs = 20)
ElbowPlot(pbmc)
PCAPlot(pbmc,group.by = "majorType")
We can compute a 2D UMAP on the 20 first components of the PCA for visualization
pbmc <-RunUMAP(pbmc, dims = c(1:20))
UMAPPlot(pbmc, group.by = "majorType")
#UMAPPlot(pbmc, group.by = "celltype") + NoLegend()
We make a metacell Seurat object from the single cells of the sample.
We assign metacell to metadata label (majorType,
celltype) with the maximum absolute abundance within
metacell thanks to the supercell_assign* function. We
compute purity of metacells according to their assignment to the
majorType annotation.
makeSeuratSC <- function(pbmc,
genes = NULL,
metaFields = c("majorType","sampleID","Age","Sex","celltype","PatientID","SARS.CoV.2","Outcome","datasets","CoVID.19.severity","Sample.time"),
returnSC = F) {
if(is.null(genes)) {
genes <- VariableFeatures(pbmc)
}
SC <- SCimplify(GetAssayData(pbmc,slot = "data"), # normalized gene expression matrix
n.pc = 20,
k.knn = 5, # number of nearest neighbors to build kNN network
gamma = 20, # graining level
genes.use = genes )# will be the ones used for integration if input is seurat integrated data
SC$purity <- supercell_purity(clusters = pbmc$majorType,
supercell_membership = SC$membership)
#boxplot(SC$purity)
for (m in metaFields) {
SC[[m]]<- supercell_assign(clusters = pbmc@meta.data[,m], # single-cell assigment to cell lines (clusters)
supercell_membership = SC$membership, # single-cell assignment to super-cells
method = "absolute")
}
GE <- supercell_GE(as.matrix(GetAssayData(pbmc,slot = "data")),groups = SC$membership)
seuratSC <- supercell_2_Seurat(SC.GE = GE,SC,fields = c(metaFields,"purity"))
res <- seuratSC
if (returnSC) {
res <- list(seuratSC = seuratSC,SC = SC)
}
return(res)
}
supercells <- makeSeuratSC(pbmc,returnSC = T)
## [1] "Done: NormalizeData"
## [1] "Doing: data to normalized data"
## [1] "Doing: weighted scaling"
## [1] "Done: weighted scaling"
We use the Seurat workflow to analyse the sample at the metacell
level. Data scaling and PCA computation are weighted according to
metacell size with the supercell_2_Seurat function we just
used.
seuratSC <- supercells$seuratSC
seuratSC <- RunUMAP(seuratSC,dims = c(1:20))
We compute the UMAP dimension reduction on the weighted PCA results for visualization.
UMAPPlot(seuratSC,group.by = "majorType")
We can use the Seurat workflow without taking into account the sample weights by restarting from the data scaling step. This will overwrite the weighted downstream analysis results in the Seurat object.
seuratSC <- ScaleData(seuratSC)
seuratSC <- RunPCA(seuratSC, npcs = 20)
seuratSC <- RunUMAP(seuratSC,dims = c(1:10))
UMAPPlot(seuratSC,group.by = "majorType")
To be noted that the splits in the CD8 and DC majorType assigned
metacells can be explained by the second level of clustering provided in
the original study (celltype column in the metadata).
UMAPPlot(seuratSC,group.by = "celltype") + NoLegend()
We can check the distribution of metacell purities and sizes obtained.
ggplot(seuratSC@meta.data,aes(x=orig.ident,y=purity)) + geom_boxplot() +
ggplot(seuratSC@meta.data,aes(x=orig.ident,y=size)) + geom_boxplot()
table(pbmc$majorType)
##
## B CD4 CD8 DC Mega Mono NK Neu Plasma
## 586 3162 3453 33 119 3273 2103 4 125
table(seuratSC$majorType)
##
## B CD4 CD8 DC Mega Mono NK Plasma
## 42 151 177 4 9 161 87 12
Neu <- colnames(pbmc[,pbmc$majorType == "Neu"])
Neu
## [1] "ATTCTACTCATGTCCC-113" "CCATTCGCATACGCCG-113" "CCGGGATTCGTGGGAA-113"
## [4] "TTGTAGGTCTGCTGTC-113"
seuratSC$majorType[supercells$SC$membership[Neu]]
## 399 195 354 429
## "Mono" "Mono" "Mono" "Mono"
We can see that for this PBMC sample the 4 single cell annotated as
Neutrophils in the original study were merged with Monocytes at the
metacell level. To completely match the original study results one
possible strategy would be to make metacells per PBMC types or to use
the cell.split.condition option of the
SCimplify function. This will result in completely pure
metacells according to the original annotation.
The drop out noise is reduced with metacell analysis.
VlnPlot(pbmc, features = 'nFeature_RNA')
VlnPlot(seuratSC, feature = 'nFeature_RNA')
This results in a better observation of gene-gene expression correlation. To illustrate this we can look for correlations of expression of CXCL8, one cytokine that came out of the original study that migh be involved in severe COVID19, and transcription factors that potentially regulate it.
gene<-as.matrix(GetAssayData(seuratSC)["CXCL8",])
correlations<-apply(as.matrix(GetAssayData(seuratSC)),1,function(x){cor(gene,x)})
tf <- read.table("../data/transcription.factor.activity.GO0003700.symbol.list")
correlations <- correlations[names(correlations) %in% tf$V1]
head(sort(abs(correlations),decreasing = T),n = 10)
## ZNF467 KLF10 KLF4 SPI1 CREB5 GAS7 ZNF385A FOS
## 0.7719651 0.7452915 0.7162176 0.7162118 0.7122388 0.7077978 0.7016751 0.7013149
## CEBPD BCL6
## 0.6821084 0.6713770
FeatureScatter(object = pbmc, feature1 = "CXCL8", feature2 = 'KLF10',group.by = "majorType")
FeatureScatter(object = seuratSC, feature1 = "CXCL8", feature2 = 'KLF10',group.by = "majorType")
FeatureScatter(object = pbmc, feature1 = "CXCL8", feature2 = 'FOS',group.by = "majorType")
FeatureScatter(object = seuratSC, feature1 = "CXCL8", feature2 = 'FOS',group.by = "majorType")
We can also use the
supercell_GeneGenePlot to have a view
of the size of the metacells. It outputs a weighted correlation
according to supercell sizes.
supercell_GeneGenePlot(GetAssayData(seuratSC),
gene_x = "CXCL8",gene_y = "FOS",
supercell_size = seuratSC$size,
clusters = seuratSC$majorType)
## $p
##
## $w.cor
## $w.cor$CXCL8_FOS
## [1] 0.7602999
##
##
## $w.pval
## $w.pval$CXCL8_FOS
## [1] 0
supercell_GeneGenePlot(GetAssayData(seuratSC),
gene_x = "CXCL8",gene_y = "KLF10",
supercell_size = seuratSC$size,
clusters = seuratSC$majorType)
## $p
##
## $w.cor
## $w.cor$CXCL8_KLF10
## [1] 0.8023985
##
##
## $w.pval
## $w.pval$CXCL8_KLF10
## [1] 0
We can also better appreciate the anticorrelation between CCND2 cycline gene and CDKN1A, coding for a cell cycle inhibitor proteine.
FeatureScatter(object = pbmc, feature1 = "CCND2", feature2 = 'CDKN1A',group.by = "majorType")
supercell_GeneGenePlot(GetAssayData(seuratSC),
gene_x = "CCND2",gene_y = "CDKN1A",
supercell_size = seuratSC$size,
clusters = seuratSC$majorType)
## $p
##
## $w.cor
## $w.cor$CCND2_CDKN1A
## [1] -0.3245677
##
##
## $w.pval
## $w.pval$CCND2_CDKN1A
## [1] 3.805634e-313
At least 16GB of memory are required to complete this analysis.
If you have more than 20GB, you should be able to integrate all the
files we provided into ../data/pbmc_COVID19_sce. (26 data
sets, about 200,000 unique cells).
If you have less than 20GB of memory, you should be able to integrate
all the male samples (which are more evenly distributed across the
different conditions than the female samples). We provide you with the
list of male samples in the file ../data/lowMemFileList.rds
(17 data sets, about 130,000 individual cells).
If you have less than 16GB of memory, you can try to integrate a small subset of 5~6 samples, but the downstream analysis we propose will not be very meaningful.
We will now integrate at the metacell level a subset of 26 samples of fresh PBMCs from the original study gathering around 200 000 cells.
We will use a gamma of 20 (10 000 metacells if you process the 26 data sets).
files <- list.files("../data/pbmc_COVID19_sce",full.names = T)
# Uncomment if you have less than 20GB of memory
# files <- readRDS("../data/lowMemFileList.rds")
# Uncomment if you less than 16GB of memory
#files <- files[c(1:6)]
SC.list <- list()
SC.list[["S-S086-2"]] <- seuratSC
for (f in files[-which(files == "../data/pbmc_COVID19_sce/S-S086-2_sce.rds")]) {
smp <- readRDS(f)
smp <- sceRawToSeurat(smp)
seuratSC <- makeSeuratSC(smp)
SC.list[[seuratSC$sampleID[1]]] <- seuratSC
}
## [1] "Done: NormalizeData"
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We select the highly variable genes accross samples for integration using the Seurat procedure.
features <- SelectIntegrationFeatures(SC.list,nfeatures = 1500)
features <- features[!features %in% gene_blacklist]
First we will use the Seurat integration workflow for large datasets using a reference based integration and the RPCA reduction dimension method.
for (i in 1:length(SC.list)) {
SC.list[[i]] <- RenameCells(SC.list[[i]],add.cell.id = unique(SC.list[[i]]$sampleID))
SC.list[[i]] <- ScaleData(SC.list[[i]],features = features)
SC.list[[i]] <- RunPCA(SC.list[[i]] ,features = features,npcs = 20)
}
The subset of samples come from male and female patients. As gender is often a large source of variation at the transcriptomic level, we will use the largest male and female samples as reference.
reference <- which(c("S-S086-2","S-M064") %in% names(SC.list))
anchors <- FindIntegrationAnchors(object.list = SC.list,
reference = reference,
reduction = "rpca",
anchor.features = features,
dims = 1:20)
integrated <- IntegrateData(anchorset = anchors, dims = 1:20)
We can now use the classical Seurat workflow on the integrated object
DefaultAssay(integrated) = "integrated"
integrated <- ScaleData(integrated, verbose = T)
integrated <- RunPCA(integrated, verbose = FALSE)
integrated <- RunUMAP(integrated, dims = 1:20)
We check that our integration correctly mixed the different samples in the reduced space by preserving differences between the different PBMC types. We can also check that we don’t have a gender effect in the integrated data.
UMAPPlot(integrated,group.by = "sampleID")
UMAPPlot(integrated,group.by = "majorType")
UMAPPlot(integrated,group.by = "Sex")
Now we will use Harmony, an other integration method. We will use it inside the Seurat framework.
DefaultAssay(integrated) <- "RNA"
integrated <- ScaleData(integrated,features = features)
integrated <- RunPCA(integrated, npcs = 20, verbose = FALSE,features = features)
integrated <- RunHarmony(integrated,c("sampleID","datasets"),theta = c(2.5,1.5))
integrated <- RunUMAP(integrated,reduction = "harmony",
reduction.name = "umap.harmony",
dims = 1:20)
DimPlot(integrated,group.by = "sampleID", reduction = "umap.harmony")
DimPlot(integrated,group.by = "majorType", reduction = "umap.harmony")
The original study mainly focused on the analysis of variations in the cell type composition according to COVID19 severity (control, mild/moderate, severe) and the time of sampling (control, progression, convalescence).
DimPlot(integrated,group.by = "Sample.time", reduction = "umap.harmony")
DimPlot(integrated,group.by = "CoVID.19.severity", reduction = "umap.harmony")
In this tutorial we will make a short analysis of this at the metacell level regarding the first level of annotation (PMBC major type). We can compute the observed versus expected cell number ratio for each PBMC type for the different conditions. We have to take into account the supercell size for this analysis.
majorTypeCounts <- aggregate(integrated$size, by=list(majorType = integrated$majorType,Severity = integrated$CoVID.19.severity,Time = integrated$Sample.time), FUN=sum)
majorTypeCounts$group = paste(majorTypeCounts$Severity,majorTypeCounts$Time,sep = "_")
contingencyTable <- xtabs(x ~ group + majorType,data = majorTypeCounts)
res <- chisq.test(contingencyTable)
Roe <- res$observed/res$expected
As in the original study we observe an increase in the Megakaryocytes cell proportion during the progression of severe/critical COVID compare to the other condition.
melted_Roe <- melt(Roe, na.rm = TRUE)
# Heatmap
ggplot(data = melted_Roe, aes(majorType,group, , fill = value))+
geom_tile() + scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 1, space = "Lab",
name="Ro/e") +
theme_minimal()+
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1))+
coord_fixed()
Without the few metacell assigned to macrophages found in the control
samples that overwrite the results:
ggplot(data = melted_Roe[melted_Roe$majorType != "Macro",], aes(majorType,group, , fill = value))+
geom_tile() + scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 1, space = "Lab",
name="Ro/e") +
theme_minimal()+
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1))+
coord_fixed()
We can do a differentially expressed gene analysis to characterized the gene markers of this population that seem to support progression of severe/critical COVID. We perform this analysis at the metacell level (on averaged normalized counts per metacell).
Idents(integrated) <- "majorType"
markersMega <- FindMarkers(integrated,ident.1 = "Mega",only.pos = T)
markersMega <- markersMega[markersMega$p_val_adj< 0.05,]
markersMega[order(markersMega$avg_log2FC,decreasing = T),]
## p_val avg_log2FC pct.1 pct.2 p_val_adj
## PPBP 7.343396e-78 6.9739255 1.000 0.542 2.051965e-73
## TUBB1 1.369173e-118 5.6194829 1.000 0.250 3.825880e-114
## PF4 1.071578e-90 5.6174591 1.000 0.387 2.994310e-86
## HIST1H2AC 2.864705e-72 5.3653535 1.000 0.731 8.004846e-68
## CAVIN2 2.357085e-99 5.2753922 1.000 0.329 6.586401e-95
## ACRBP 1.829055e-102 5.2367713 1.000 0.313 5.110928e-98
## RGS18 1.818439e-79 4.9225969 1.000 0.506 5.081265e-75
## GP9 5.546363e-174 4.8720905 1.000 0.147 1.549820e-169
## TSC22D1 6.999086e-96 4.7060554 1.000 0.348 1.955755e-91
## MPIG6B 4.634917e-117 4.6369998 0.991 0.246 1.295135e-112
## SPARC 5.065096e-98 4.5387751 1.000 0.335 1.415340e-93
## GNG11 1.338831e-111 4.5275871 1.000 0.273 3.741097e-107
## TMEM40 1.451894e-179 4.1880384 0.991 0.138 4.057029e-175
## TUBA4A 1.173480e-68 4.0509572 1.000 0.906 3.279054e-64
## CLU 9.698693e-77 3.9565073 0.981 0.465 2.710106e-72
## PTCRA 9.085600e-300 3.9356358 0.991 0.071 2.538789e-295
## CMTM5 2.390091e-229 3.9287965 1.000 0.103 6.678631e-225
## MMD 2.652647e-83 3.9124558 1.000 0.453 7.412291e-79
## NRGN 1.110951e-77 3.8322352 1.000 0.542 3.104330e-73
## TPM4 3.322777e-65 3.8132802 1.000 0.912 9.284837e-61
## MPP1 6.369813e-73 3.7923157 1.000 0.602 1.779917e-68
## GRAP2 3.497336e-73 3.7904823 1.000 0.621 9.772606e-69
## RGS10 5.455530e-68 3.7545251 1.000 0.916 1.524439e-63
## RAP1B 3.782094e-67 3.7161719 1.000 0.985 1.056831e-62
## LIMS1 3.802598e-66 3.7126724 0.991 0.843 1.062560e-61
## MYL9 1.719241e-106 3.7064382 0.972 0.257 4.804075e-102
## HIST1H3H 7.506411e-103 3.6625307 0.991 0.291 2.097517e-98
## YWHAH 7.347504e-64 3.6166561 0.991 0.781 2.053113e-59
## NT5C3A 3.086653e-67 3.5764202 1.000 0.865 8.625034e-63
## ESAM 5.776516e-181 3.5690281 0.972 0.129 1.614132e-176
## MAX 1.310628e-66 3.5599763 1.000 0.952 3.662287e-62
## PTGS1 9.981906e-95 3.5522458 1.000 0.356 2.789244e-90
## TRIM58 1.351251e-127 3.5439518 0.972 0.203 3.775800e-123
## CTSA 2.709868e-67 3.4666203 1.000 0.885 7.572185e-63
## RUFY1 3.777995e-73 3.4400121 1.000 0.628 1.055685e-68
## F13A1 2.399869e-84 3.4110772 0.981 0.378 6.705955e-80
## CLEC1B 2.431521e-121 3.3829744 0.981 0.222 6.794399e-117
## HIST1H2BJ 7.458737e-126 3.3276762 1.000 0.226 2.084195e-121
## TAGLN2 2.059878e-68 3.2857938 1.000 0.998 5.755918e-64
## C2orf88 1.119232e-131 3.2634341 0.963 0.190 3.127471e-127
## TREML1 2.764922e-157 3.2232758 0.981 0.158 7.726023e-153
## PGRMC1 4.832107e-72 3.1830420 1.000 0.685 1.350236e-67
## ITGA2B 4.956780e-161 3.1756464 0.991 0.158 1.385073e-156
## RAB11A 9.959685e-66 3.1706711 1.000 0.940 2.783035e-61
## MAP3K7CL 4.190299e-73 3.1560255 0.981 0.514 1.170895e-68
## NCOA4 1.248128e-62 3.1379038 1.000 0.926 3.487644e-58
## GSTO1 2.835036e-56 3.1079719 0.991 0.920 7.921940e-52
## TLN1 1.215672e-59 3.0575100 1.000 0.961 3.396951e-55
## FERMT3 9.961996e-56 3.0247397 0.991 0.952 2.783680e-51
## VCL 7.676626e-67 3.0100747 0.991 0.782 2.145080e-62
## PDLIM1 1.498277e-77 3.0001736 0.991 0.445 4.186636e-73
## PTPN18 1.603420e-62 2.9325477 0.991 0.852 4.480436e-58
## BEX3 1.146923e-84 2.8819700 1.000 0.433 3.204847e-80
## NAP1L1 1.496767e-66 2.8804706 1.000 0.983 4.182416e-62
## AP003068.2 3.843168e-109 2.8696867 0.972 0.251 1.073896e-104
## TMEM140 2.250776e-65 2.8615610 0.953 0.548 6.289344e-61
## ILK 3.144326e-62 2.8564302 1.000 0.876 8.786191e-58
## KIF2A 7.775329e-67 2.7708350 1.000 0.874 2.172660e-62
## STOM 4.007211e-61 2.7260386 0.991 0.763 1.119735e-56
## ODC1 6.612067e-60 2.7174499 0.972 0.714 1.847610e-55
## NFE2 6.114892e-67 2.7152387 0.935 0.346 1.708684e-62
## RSU1 4.057717e-64 2.7077428 1.000 0.903 1.133848e-59
## CALM3 1.148436e-63 2.7035959 1.000 0.979 3.209075e-59
## FAM110A 1.205604e-43 2.6835797 0.916 0.687 3.368820e-39
## GPX4 8.706234e-63 2.6784640 1.000 0.988 2.432783e-58
## EIF2AK1 6.370642e-60 2.6605001 0.981 0.857 1.780148e-55
## RAB27B 2.915467e-100 2.6523426 0.963 0.268 8.146690e-96
## ETFA 1.073701e-65 2.6290838 1.000 0.881 3.000243e-61
## PF4V1 5.402475e-127 2.6257679 0.953 0.190 1.509614e-122
## MARCH2 4.760433e-76 2.6157620 1.000 0.546 1.330208e-71
## DAB2 9.110602e-86 2.6140086 0.953 0.322 2.545775e-81
## DMTN 4.356634e-139 2.6091859 0.925 0.156 1.217374e-134
## OAZ1 1.138064e-57 2.5977175 1.000 0.999 3.180093e-53
## NEXN 3.341564e-76 2.5767199 0.925 0.348 9.337332e-72
## SMOX 6.432445e-206 2.5564662 0.972 0.110 1.797418e-201
## SLC40A1 6.549881e-75 2.5247252 0.972 0.427 1.830233e-70
## CA2 1.503361e-106 2.5120752 0.953 0.240 4.200842e-102
## MYLK 1.297477e-108 2.5095504 0.963 0.246 3.625541e-104
## CDKN1A 3.017902e-64 2.4844153 0.981 0.486 8.432924e-60
## GUCY1B1 2.011203e-127 2.4763974 0.972 0.203 5.619904e-123
## OST4 9.905373e-69 2.4550259 1.000 0.998 2.767858e-64
## R3HDM4 4.514358e-61 2.4527496 1.000 0.883 1.261447e-56
## ADIPOR1 4.755157e-53 2.4494513 0.963 0.860 1.328733e-48
## SMIM3 4.528998e-79 2.4282733 0.991 0.461 1.265538e-74
## RNF11 4.033438e-59 2.4259965 0.981 0.761 1.127064e-54
## PLA2G12A 1.888402e-72 2.4057637 0.981 0.541 5.276763e-68
## LMNA 5.342916e-73 2.3921686 0.963 0.415 1.492971e-68
## H2AFJ 1.938963e-55 2.3864362 0.981 0.882 5.418046e-51
## ACTN1 7.939342e-65 2.3702170 0.972 0.621 2.218490e-60
## PRKAR2B 1.026559e-113 2.3338496 0.981 0.246 2.868513e-109
## H3F3A 2.366433e-56 2.3156230 1.000 1.000 6.612523e-52
## CNST 5.520655e-46 2.3126812 0.944 0.809 1.542636e-41
## GAS2L1 3.573659e-108 2.3062019 0.916 0.206 9.985875e-104
## SNCA 1.292524e-106 2.2976775 0.972 0.258 3.611699e-102
## GMPR 4.004290e-119 2.2922754 0.963 0.213 1.118919e-114
## RASGRP2 1.419702e-54 2.2792865 0.991 0.964 3.967074e-50
## TPST2 1.916056e-50 2.2720952 0.963 0.854 5.354035e-46
## AC147651.1 6.221279e-219 2.2422547 0.963 0.099 1.738412e-214
## WBP2 3.587594e-61 2.2303000 1.000 0.920 1.002481e-56
## TMBIM1 1.284256e-54 2.2275085 0.981 0.887 3.588597e-50
## YWHAZ 6.499557e-59 2.2200041 1.000 0.999 1.816171e-54
## CCND3 1.060959e-47 2.2166474 1.000 0.983 2.964639e-43
## ZYX 9.782530e-32 2.2115371 1.000 0.935 2.733532e-27
## PRDX6 4.865923e-59 2.2107954 1.000 0.966 1.359685e-54
## CARD19 6.965770e-63 2.2031097 0.981 0.700 1.946445e-58
## WDR1 2.214920e-58 2.1763029 1.000 0.968 6.189151e-54
## DNAJB6 3.876991e-49 2.1732361 0.991 0.937 1.083347e-44
## CD9 5.442145e-76 2.1670729 0.925 0.328 1.520699e-71
## SNAP23 1.034429e-61 2.1644924 0.991 0.897 2.890504e-57
## GP1BA 0.000000e+00 2.1586664 0.907 0.052 0.000000e+00
## ARHGAP18 1.641839e-67 2.1486918 0.972 0.566 4.587790e-63
## CMIP 3.806205e-58 2.1260658 0.972 0.716 1.063568e-53
## RAB32 3.187432e-67 2.1173491 0.925 0.357 8.906640e-63
## PARVB 9.435945e-44 2.1059391 0.888 0.605 2.636686e-39
## LAMTOR1 5.725636e-56 2.0795333 1.000 0.976 1.599914e-51
## MFSD1 2.442529e-60 2.0701596 0.991 0.816 6.825158e-56
## RIOK3 1.143793e-45 2.0691391 0.944 0.921 3.196102e-41
## TUBA1C 1.758646e-47 2.0489714 0.972 0.858 4.914185e-43
## ITM2B 2.550433e-51 2.0440351 1.000 0.997 7.126675e-47
## LINC00989 3.007613e-222 2.0422163 0.963 0.096 8.404174e-218
## TPM1 3.606997e-68 2.0352095 0.944 0.470 1.007903e-63
## DAPP1 3.905654e-57 2.0334741 0.981 0.658 1.091357e-52
## RIPOR2 6.578503e-42 2.0291592 1.000 0.991 1.838231e-37
## MYL12A 9.328502e-54 2.0288038 1.000 0.999 2.606663e-49
## TUBA8 0.000000e+00 2.0273743 0.963 0.048 0.000000e+00
## TIMP1 3.948581e-50 2.0184633 1.000 0.870 1.103352e-45
## FRMD3 4.026812e-67 2.0135126 0.935 0.425 1.125212e-62
## XPNPEP1 1.236807e-55 2.0049435 0.963 0.693 3.456009e-51
## ARHGAP6 3.695517e-221 1.9871985 0.991 0.105 1.032638e-216
## CTTN 2.152597e-139 1.9861507 0.935 0.160 6.015002e-135
## LAT 5.593737e-50 1.9785757 0.991 0.727 1.563058e-45
## AKIRIN2 2.182696e-43 1.9627046 0.972 0.919 6.099106e-39
## CAPZA2 1.207281e-55 1.9266343 1.000 0.944 3.373506e-51
## NDUFA6 5.062248e-49 1.9263512 1.000 0.953 1.414544e-44
## CD99 8.374875e-43 1.9250567 1.000 0.982 2.340191e-38
## ICAM2 2.557495e-43 1.9201025 0.981 0.900 7.146408e-39
## TNS1 1.400846e-117 1.9081667 0.907 0.178 3.914383e-113
## ITGB3 3.286520e-257 1.9035641 0.888 0.066 9.183524e-253
## CLDN5 0.000000e+00 1.8960807 0.841 0.037 0.000000e+00
## GFI1B 2.117830e-174 1.8778844 0.935 0.121 5.917853e-170
## HIST2H2AA4 1.204453e-44 1.8674150 0.916 0.615 3.365603e-40
## THBS1 4.768385e-49 1.8500415 0.925 0.633 1.332430e-44
## BIN2 8.662594e-38 1.8458416 1.000 0.978 2.420589e-33
## AC114752.2 2.174391e-261 1.8442714 0.897 0.066 6.075901e-257
## GLUL 3.291718e-33 1.8290017 0.944 0.815 9.198047e-29
## NORAD 4.447716e-58 1.8270190 0.991 0.880 1.242825e-53
## LDLRAP1 3.529597e-49 1.8200090 0.953 0.647 9.862753e-45
## ARF1 5.933444e-49 1.8143296 1.000 0.993 1.657982e-44
## AP001189.1 1.443939e-173 1.8063541 0.963 0.131 4.034798e-169
## MGLL 9.098840e-110 1.7946134 0.944 0.220 2.542489e-105
## EGFL7 1.082499e-185 1.7895811 0.841 0.085 3.024827e-181
## SNN 9.761356e-57 1.7796474 0.925 0.501 2.727616e-52
## DERA 2.505083e-47 1.7721738 0.907 0.646 6.999955e-43
## AC090409.1 0.000000e+00 1.7671706 0.953 0.039 0.000000e+00
## FAXDC2 2.941422e-178 1.7654674 0.925 0.115 8.219217e-174
## TGFB1 2.438165e-54 1.7513295 1.000 0.949 6.812964e-50
## STON2 1.004057e-199 1.7502433 0.935 0.102 2.805636e-195
## CDKN2D 1.109487e-38 1.7501688 0.944 0.852 3.100240e-34
## ANO6 1.286249e-57 1.7419751 0.972 0.751 3.594165e-53
## MED12L 1.036314e-200 1.7320790 0.897 0.091 2.895771e-196
## SEC14L1 1.783657e-38 1.7196218 0.972 0.849 4.984073e-34
## PKM 1.576644e-32 1.7047148 0.981 0.990 4.405615e-28
## ALOX12 4.831236e-233 1.7001863 0.944 0.087 1.349992e-228
## SH3BGRL2 3.713423e-169 1.6992608 0.925 0.123 1.037642e-164
## PDCD10 2.319903e-49 1.6910744 0.972 0.908 6.482505e-45
## C19orf33 2.594053e-207 1.6770046 0.822 0.071 7.248562e-203
## HIST1H1C 3.790627e-44 1.6715367 0.944 0.788 1.059215e-39
## TLK1 1.781220e-63 1.6594407 1.000 0.839 4.977262e-59
## PTGIR 4.827762e-47 1.6583668 0.850 0.431 1.349022e-42
## TALDO1 1.271365e-38 1.6512866 1.000 0.964 3.552574e-34
## CCNG1 9.795723e-35 1.6481784 0.925 0.869 2.737219e-30
## MIR4435-2HG 1.507140e-55 1.6453737 0.972 0.655 4.211402e-51
## VDAC3 3.952310e-57 1.6449944 1.000 0.903 1.104394e-52
## IFRD1 1.099848e-38 1.6436645 0.916 0.801 3.073306e-34
## CORO1C 6.649611e-56 1.6421629 0.944 0.536 1.858101e-51
## FTH1 8.265325e-43 1.6324280 1.000 1.000 2.309580e-38
## RDH11 3.235857e-53 1.6291787 0.953 0.792 9.041956e-49
## DYNLL1 5.615484e-44 1.6215982 1.000 0.959 1.569135e-39
## BCL2L1 1.067458e-51 1.6186812 0.916 0.601 2.982797e-47
## RBX1 2.037297e-47 1.6178705 1.000 0.973 5.692820e-43
## INKA1 9.306606e-134 1.6002047 0.813 0.114 2.600545e-129
## LYL1 2.721129e-46 1.5910366 0.850 0.432 7.603650e-42
## TNFSF4 2.686233e-268 1.5841580 0.832 0.053 7.506140e-264
## GP6 2.854612e-237 1.5806140 0.907 0.077 7.976644e-233
## VIM-AS1 3.418000e-84 1.5732294 0.981 0.378 9.550919e-80
## NAT8 0.000000e+00 1.5732050 0.804 0.030 0.000000e+00
## CAP1 3.168936e-39 1.5708896 1.000 0.981 8.854958e-35
## MINDY1 4.763359e-75 1.5663388 0.916 0.343 1.331025e-70
## LTBP1 0.000000e+00 1.5656600 0.897 0.034 0.000000e+00
## HIST1H2BC 1.546339e-63 1.5642955 0.935 0.470 4.320934e-59
## MTPN 4.456621e-37 1.5557001 0.963 0.966 1.245313e-32
## ABCC3 3.240209e-112 1.5520566 0.963 0.228 9.054115e-108
## EMC3 7.363636e-58 1.5478175 0.991 0.916 2.057621e-53
## ZNF185 5.054147e-84 1.5463074 0.925 0.294 1.412280e-79
## SMIM5 1.723366e-234 1.5461303 0.888 0.073 4.815602e-230
## SLA2 1.429612e-57 1.5440242 0.944 0.514 3.994765e-53
## CD226 1.142727e-58 1.5329830 0.935 0.544 3.193122e-54
## ELF1 1.021507e-26 1.5321534 0.953 0.973 2.854397e-22
## SQSTM1 1.963371e-34 1.5268909 0.991 0.970 5.486248e-30
## ENKUR 3.081702e-262 1.5137902 0.860 0.059 8.611199e-258
## LGALS12 2.022312e-175 1.5018363 0.897 0.107 5.650947e-171
## FRMD4B 1.011526e-46 1.4983496 0.860 0.434 2.826507e-42
## AL731557.1 0.000000e+00 1.4876766 0.981 0.057 0.000000e+00
## NUTF2 2.413745e-46 1.4859142 0.972 0.905 6.744727e-42
## IFI27 1.703478e-15 1.4831030 0.664 0.401 4.760027e-11
## HLA-E 2.572529e-46 1.4755627 1.000 1.000 7.188418e-42
## CAPN1 1.387566e-46 1.4744873 0.944 0.883 3.877275e-42
## SOD2 1.928668e-44 1.4737044 0.981 0.930 5.389276e-40
## SLC44A2 3.801980e-48 1.4583548 0.963 0.890 1.062387e-43
## LEPROT 2.711495e-54 1.4556585 0.972 0.783 7.576732e-50
## CYTOR 3.572011e-42 1.4448973 0.953 0.799 9.981270e-38
## PNMA1 1.563690e-63 1.4446440 0.897 0.400 4.369419e-59
## HIST1H4H 7.180020e-23 1.4418265 0.617 0.343 2.006313e-18
## GATA1 0.000000e+00 1.4388256 0.794 0.012 0.000000e+00
## GADD45A 8.872310e-60 1.4288636 0.897 0.413 2.479190e-55
## LYPLAL1 2.050746e-43 1.4285988 0.897 0.651 5.730398e-39
## PSTPIP2 1.466180e-51 1.4274807 0.944 0.609 4.096946e-47
## SIAH2 1.482725e-16 1.4241101 0.701 0.569 4.143178e-12
## PIP4P2 7.257828e-60 1.4234403 0.963 0.591 2.028055e-55
## LCN2 4.039321e-297 1.4222448 0.869 0.052 1.128708e-292
## AC123912.4 2.527945e-232 1.4098637 0.776 0.054 7.063838e-228
## AP000547.3 1.366486e-92 1.4079876 0.925 0.261 3.818373e-88
## HIST1H2BH 1.190993e-99 1.4022654 0.822 0.162 3.327991e-95
## INSIG1 7.843459e-42 1.3994269 0.925 0.712 2.191698e-37
## MOB1B 8.643112e-55 1.3942843 0.897 0.486 2.415145e-50
## HACD4 5.912853e-45 1.3912950 0.972 0.910 1.652228e-40
## GSN 5.500991e-46 1.3877797 0.935 0.568 1.537142e-41
## MAPRE2 2.195241e-45 1.3785170 0.972 0.872 6.134162e-41
## YPEL5 2.791833e-51 1.3781474 1.000 0.941 7.801218e-47
## TMEM50A 1.694031e-43 1.3731437 0.981 0.982 4.733631e-39
## LGALSL 0.000000e+00 1.3681928 0.869 0.050 0.000000e+00
## HIST1H2AG 1.443853e-58 1.3656214 0.841 0.324 4.034559e-54
## TMEM91 1.765846e-57 1.3550905 0.944 0.492 4.934304e-53
## SH3BGRL3 1.755442e-22 1.3471029 1.000 1.000 4.905232e-18
## RAP2B 1.684371e-36 1.3434418 0.944 0.822 4.706637e-32
## FHL1 2.311710e-73 1.3341024 0.860 0.272 6.459610e-69
## CMTM6 5.600453e-39 1.3318405 0.981 0.946 1.564935e-34
## SPNS1 1.703098e-51 1.3310926 0.944 0.733 4.758965e-47
## NDUFS5 1.411172e-30 1.3230913 0.991 0.986 3.943238e-26
## MKRN1 1.475250e-40 1.3189781 0.963 0.920 4.122291e-36
## SELP 0.000000e+00 1.3125252 0.925 0.051 0.000000e+00
## MOB1A 1.177983e-36 1.3124526 1.000 0.961 3.291637e-32
## C9orf16 5.276114e-41 1.3069542 0.991 0.961 1.474305e-36
## CYB5R3 3.232918e-45 1.3058304 0.972 0.834 9.033744e-41
## PDE5A 2.045737e-127 1.3033997 0.935 0.180 5.716402e-123
## MMRN1 0.000000e+00 1.3029894 0.822 0.039 0.000000e+00
## MCUR1 2.222865e-49 1.3015478 0.916 0.667 6.211353e-45
## EHD3 7.813802e-112 1.3008182 0.897 0.186 2.183411e-107
## MYH9 2.117303e-26 1.2975873 0.972 0.991 5.916381e-22
## ACTB 3.748459e-20 1.2945203 1.000 1.000 1.047432e-15
## SCN1B 3.583513e-110 1.2904813 0.935 0.215 1.001341e-105
## ENDOD1 4.677594e-71 1.2902699 0.925 0.387 1.307060e-66
## MYL6 1.215946e-18 1.2864160 1.000 1.000 3.397718e-14
## FKBP1A 5.744507e-33 1.2837066 1.000 0.975 1.605188e-28
## CCL5 5.422780e-28 1.2823672 1.000 0.785 1.515287e-23
## CD151 2.348147e-39 1.2718742 0.925 0.732 6.561427e-35
## RAB1B 2.054168e-36 1.2710733 0.944 0.937 5.739962e-32
## ARPC5 2.950163e-37 1.2694750 1.000 0.989 8.243641e-33
## TBXA2R 3.045929e-109 1.2676533 0.822 0.150 8.511240e-105
## RNF24 9.008066e-47 1.2637197 0.925 0.590 2.517124e-42
## TAL1 1.167523e-195 1.2597928 0.879 0.090 3.262408e-191
## MBNL1 3.084731e-22 1.2552899 1.000 0.996 8.619664e-18
## TUBB4B 1.354919e-37 1.2533300 0.972 0.876 3.786049e-33
## SSX2IP 1.778223e-74 1.2478650 0.850 0.265 4.968889e-70
## SVIP 5.358926e-56 1.2395616 0.963 0.680 1.497445e-51
## RAB4A 2.771518e-52 1.2374265 0.972 0.793 7.744453e-48
## TBPL1 6.763364e-31 1.2252158 0.888 0.793 1.889887e-26
## PLEK 1.969687e-17 1.2249602 0.981 0.848 5.503895e-13
## VAPA 4.464618e-29 1.2101092 0.981 0.974 1.247548e-24
## RAB37 1.622035e-48 1.2071530 0.963 0.754 4.532452e-44
## MLH3 7.519624e-73 1.2056115 0.963 0.462 2.101209e-68
## MFAP3L 0.000000e+00 1.2048223 0.888 0.047 0.000000e+00
## SPINT2 1.227283e-45 1.2044963 0.944 0.642 3.429396e-41
## MEIS1 2.001635e-75 1.1898404 0.738 0.171 5.593168e-71
## ELOVL7 0.000000e+00 1.1889815 0.897 0.053 0.000000e+00
## PYGL 1.247177e-51 1.1711203 0.907 0.389 3.484988e-47
## UBL4A 3.261687e-55 1.1667532 0.953 0.610 9.114133e-51
## WDR66 6.824025e-38 1.1665505 0.794 0.449 1.906837e-33
## BNIP2 4.703447e-32 1.1649460 0.981 0.924 1.314284e-27
## TPM3 4.783579e-28 1.1625683 1.000 0.999 1.336675e-23
## PRKAR1B 4.034469e-74 1.1585699 0.944 0.398 1.127352e-69
## ARPC1B 1.983816e-18 1.1576877 0.991 0.992 5.543377e-14
## F2R 5.638232e-52 1.1532982 0.897 0.420 1.575491e-47
## P2RX1 1.062986e-62 1.1501725 0.925 0.368 2.970302e-58
## SNX3 1.709566e-34 1.1413515 0.991 0.949 4.777040e-30
## MTURN 5.243275e-108 1.1402873 0.935 0.223 1.465128e-103
## DNM3 6.201641e-228 1.1383222 0.888 0.076 1.732925e-223
## AC010186.1 1.130407e-225 1.1288172 0.832 0.066 3.158696e-221
## HIST2H2BE 2.591464e-50 1.1270913 0.860 0.430 7.241328e-46
## SAT1 1.017476e-25 1.1235738 1.000 0.991 2.843133e-21
## KCTD20 3.958358e-44 1.1187479 0.925 0.744 1.106084e-39
## PPM1A 5.441020e-47 1.1184609 0.944 0.731 1.520384e-42
## SRGN 2.141148e-20 1.1115565 0.991 0.975 5.983009e-16
## CLIC1 2.133554e-14 1.1089035 1.000 0.991 5.961791e-10
## KIFC3 1.422963e-90 1.1058378 0.841 0.195 3.976185e-86
## CRBN 1.879219e-14 1.1023571 0.897 0.911 5.251103e-10
## SLC2A3 6.044119e-23 1.0984183 0.953 0.906 1.688908e-18
## TACC3 3.001535e-38 1.0942012 0.916 0.715 8.387190e-34
## IGF2BP3 1.125150e-128 1.0919475 0.785 0.111 3.144007e-124
## CCDC85B 5.456953e-17 1.0902718 0.925 0.888 1.524836e-12
## DEPP1 3.078471e-80 1.0865794 0.262 0.016 8.602172e-76
## APP 5.860649e-51 1.0863149 0.953 0.624 1.637641e-46
## AP001636.3 3.312130e-183 1.0861148 0.897 0.097 9.255085e-179
## HMGB1 2.727931e-37 1.0842534 1.000 0.999 7.622658e-33
## ARF4 1.508660e-24 1.0832345 0.963 0.918 4.215650e-20
## ASAH1 5.722534e-31 1.0822665 1.000 0.925 1.599048e-26
## CYB5R1 4.149553e-39 1.0750572 0.897 0.691 1.159509e-34
## SENCR 9.972209e-78 1.0689143 0.822 0.226 2.786534e-73
## TXNL4B 1.050417e-28 1.0673825 0.720 0.456 2.935181e-24
## ITGB1 1.309744e-39 1.0664465 0.991 0.902 3.659818e-35
## RBBP6 2.381757e-13 1.0657847 0.907 0.920 6.655343e-09
## NPTN 3.504263e-29 1.0628568 0.850 0.666 9.791961e-25
## AC008763.1 6.081967e-128 1.0611146 0.813 0.121 1.699484e-123
## MAP4K5 7.763810e-36 1.0596975 0.879 0.683 2.169441e-31
## RTN3 6.764202e-38 1.0565244 0.972 0.860 1.890121e-33
## SMIM1 5.331010e-70 1.0552878 0.710 0.167 1.489644e-65
## PTTG1IP 6.107811e-30 1.0552042 0.935 0.813 1.706706e-25
## NDUFAF3 1.530760e-35 1.0543198 0.953 0.923 4.277402e-31
## C12orf75 4.967499e-29 1.0534896 0.953 0.621 1.388068e-24
## CLIC4 2.179069e-50 1.0533850 0.832 0.365 6.088973e-46
## TNNC2 1.052797e-208 1.0523739 0.869 0.080 2.941830e-204
## RAB8A 2.725876e-39 1.0511654 0.972 0.914 7.616916e-35
## ERV3-1 3.127748e-38 1.0505816 0.785 0.454 8.739866e-34
## STRAP 2.251284e-20 1.0499757 0.953 0.936 6.290762e-16
## RAB10 2.394011e-32 1.0496201 0.972 0.906 6.689586e-28
## FAH 3.102378e-52 1.0489809 0.813 0.347 8.668975e-48
## GTPBP2 1.421736e-41 1.0481495 0.785 0.407 3.972756e-37
## PAIP2 2.065052e-35 1.0448556 1.000 0.975 5.770373e-31
## MYCT1 0.000000e+00 1.0415958 0.757 0.031 0.000000e+00
## FLNA 3.313024e-07 1.0378830 0.944 0.978 9.257582e-03
## ISCA1 1.281947e-46 1.0358226 0.963 0.832 3.582144e-42
## PRUNE1 4.613683e-53 1.0352409 0.869 0.446 1.289201e-48
## CMPK1 1.125022e-42 1.0264225 0.963 0.889 3.143649e-38
## WIPF1 1.204517e-29 1.0259363 0.991 0.975 3.365781e-25
## ABLIM3 0.000000e+00 1.0256229 0.860 0.046 0.000000e+00
## DSE 3.937210e-35 1.0244637 0.832 0.526 1.100175e-30
## UBAC2 2.722726e-41 1.0230068 0.944 0.910 7.608113e-37
## ABCC4 1.114047e-59 1.0218099 0.813 0.295 3.112983e-55
## KIAA0513 3.018775e-43 1.0200822 0.897 0.494 8.435362e-39
## C12orf76 2.494354e-36 1.0147766 0.879 0.740 6.969972e-32
## ARRB1 2.898000e-27 1.0145990 0.785 0.534 8.097882e-23
## UQCRH 4.717017e-33 1.0093311 1.000 0.996 1.318076e-28
## AMD1 9.102300e-41 1.0054950 0.953 0.899 2.543456e-36
## C1orf198 2.664247e-147 0.9973188 0.785 0.094 7.444704e-143
## CLCN3 4.771460e-46 0.9963321 0.907 0.655 1.333289e-41
## MFSD2B 1.289889e-278 0.9944178 0.785 0.044 3.604336e-274
## BEND2 3.894562e-213 0.9930740 0.897 0.085 1.088257e-208
## SEPT11 5.272213e-35 0.9921772 0.832 0.560 1.473214e-30
## TM2D2 7.803014e-33 0.9916185 0.804 0.570 2.180396e-28
## INKA2 2.010704e-88 0.9886209 0.701 0.126 5.618510e-84
## TMSB4X 6.127401e-17 0.9885763 1.000 1.000 1.712180e-12
## CENPT 1.235591e-33 0.9860357 0.860 0.680 3.452611e-29
## PPDPF 3.381131e-16 0.9799610 0.991 0.976 9.447894e-12
## MAP1A 3.862205e-233 0.9792438 0.832 0.062 1.079216e-228
## GNAS 2.786887e-28 0.9785978 0.991 0.970 7.787397e-24
## AGPAT1 3.161851e-44 0.9769910 0.897 0.681 8.835159e-40
## CAMTA1 3.027794e-41 0.9744854 0.953 0.846 8.460563e-37
## HIST1H2BK 1.103098e-59 0.9729913 0.953 0.544 3.082387e-55
## PPP1R14A 2.784409e-83 0.9617653 0.748 0.142 7.780474e-79
## HEMGN 5.809395e-142 0.9581669 0.804 0.103 1.623319e-137
## EGLN3 8.196729e-128 0.9561446 0.785 0.110 2.290412e-123
## ZC3HAV1L 1.166295e-134 0.9560348 0.748 0.093 3.258977e-130
## PTPN12 3.862978e-42 0.9527721 0.925 0.645 1.079432e-37
## ANXA3 1.141744e-127 0.9441462 0.748 0.097 3.190375e-123
## KCNA3 6.554687e-24 0.9334664 0.813 0.552 1.831576e-19
## PDZK1IP1 6.770486e-262 0.9330653 0.776 0.046 1.891877e-257
## CASP3 6.174896e-22 0.9329074 0.757 0.608 1.725451e-17
## MOB3C 2.384832e-30 0.9327554 0.673 0.348 6.663935e-26
## XK 0.000000e+00 0.9325759 0.729 0.025 0.000000e+00
## SUPT4H1 1.020500e-29 0.9254309 1.000 0.943 2.851582e-25
## HBD 2.522880e-105 0.9239379 0.439 0.036 7.049683e-101
## UIMC1 9.777910e-24 0.9180396 0.888 0.834 2.732241e-19
## SWI5 5.614072e-48 0.9165638 0.897 0.579 1.568740e-43
## HK1 6.180109e-32 0.9164114 0.916 0.819 1.726908e-27
## INF2 4.801821e-47 0.9156131 0.841 0.439 1.341773e-42
## MAGED2 2.019574e-43 0.9154733 0.944 0.745 5.643297e-39
## LRRC8B 9.130960e-36 0.9117750 0.692 0.312 2.551464e-31
## UXS1 2.013726e-45 0.9112758 0.925 0.677 5.626954e-41
## HMG20B 4.528561e-38 0.9059887 0.888 0.756 1.265416e-33
## PTK2 6.940892e-62 0.9031049 0.860 0.333 1.939493e-57
## FKBP8 9.215571e-25 0.9017661 0.981 0.968 2.575107e-20
## ARG2 3.443190e-228 0.8967344 0.832 0.065 9.621305e-224
## STX11 2.357198e-23 0.8929592 0.869 0.624 6.586719e-19
## PBX1 1.135319e-104 0.8923788 0.841 0.166 3.172422e-100
## BNIP3L 3.670707e-17 0.8923093 0.935 0.906 1.025706e-12
## ACTR3B 3.947048e-59 0.8876060 0.804 0.285 1.102924e-54
## TAX1BP3 1.308281e-46 0.8866987 0.897 0.600 3.655729e-42
## SPOCD1 5.359968e-220 0.8845747 0.776 0.057 1.497736e-215
## NCK2 5.485115e-52 0.8818660 0.888 0.483 1.532706e-47
## CERS2 2.118680e-39 0.8799288 0.916 0.743 5.920227e-35
## PTP4A2 8.981008e-21 0.8784660 0.991 0.967 2.509563e-16
## GLA 1.111263e-25 0.8784293 0.757 0.566 3.105202e-21
## SERINC3 1.067657e-29 0.8775357 0.963 0.900 2.983354e-25
## ARL6IP5 2.953021e-23 0.8774279 1.000 0.988 8.251627e-19
## PPP3R1 8.671239e-40 0.8766704 0.925 0.699 2.423004e-35
## EIF1B 3.213806e-20 0.8753408 0.953 0.963 8.980337e-16
## ATP2A3 3.195173e-33 0.8746690 0.897 0.798 8.928272e-29
## AL034397.3 3.531700e-28 0.8744867 0.785 0.398 9.868628e-24
## SSBP2 6.272867e-35 0.8727896 0.888 0.719 1.752827e-30
## CD47 4.954947e-15 0.8716650 0.991 0.994 1.384561e-10
## AQP10 0.000000e+00 0.8709824 0.766 0.016 0.000000e+00
## GABARAPL2 4.554746e-28 0.8659656 0.981 0.960 1.272733e-23
## MAP2K3 1.873825e-23 0.8656328 0.907 0.847 5.236029e-19
## CGRRF1 4.275604e-17 0.8639870 0.729 0.614 1.194732e-12
## SLC10A3 1.995258e-32 0.8604387 0.813 0.611 5.575351e-28
## CAPZB 2.624566e-24 0.8599902 1.000 0.993 7.333824e-20
## VPS37B 3.539113e-24 0.8561933 0.897 0.826 9.889343e-20
## TBXAS1 1.655633e-15 0.8560195 0.841 0.591 4.626336e-11
## MEPCE 1.004070e-39 0.8550877 0.841 0.542 2.805673e-35
## UBE2E3 7.269997e-41 0.8545556 0.981 0.824 2.031455e-36
## ABHD16A 2.374634e-32 0.8494760 0.832 0.652 6.635440e-28
## EIF4G2 1.506387e-24 0.8488485 0.981 0.990 4.209297e-20
## PKHD1L1 3.948654e-185 0.8480790 0.785 0.071 1.103372e-180
## SRSF8 2.851227e-35 0.8425923 0.935 0.805 7.967185e-31
## STX7 1.227128e-15 0.8400269 0.879 0.811 3.428964e-11
## RAC1 3.506481e-18 0.8397100 0.972 0.953 9.798160e-14
## RAB31 6.251094e-29 0.8386185 0.925 0.490 1.746743e-24
## PLEKHO1 1.598012e-34 0.8360851 0.935 0.676 4.465324e-30
## EIF4E 5.333651e-21 0.8330635 0.897 0.882 1.490382e-16
## RGCC 2.016642e-20 0.8284901 0.794 0.571 5.635103e-16
## ANKRD28 4.001616e-38 0.8279327 0.869 0.587 1.118172e-33
## TFPI 4.650751e-43 0.8179927 0.832 0.464 1.299559e-38
## TSPAN18 1.427395e-103 0.8175692 0.710 0.109 3.988571e-99
## NAA38 3.463766e-22 0.8160673 0.991 0.959 9.678802e-18
## ARMCX3 3.123247e-44 0.8154768 0.925 0.712 8.727290e-40
## HTATIP2 8.406166e-19 0.8153329 0.850 0.825 2.348935e-14
## SPHK1 2.574665e-56 0.8141772 0.804 0.288 7.194387e-52
## IL6ST 2.777539e-27 0.8139960 0.925 0.783 7.761278e-23
## DAP 3.323625e-31 0.8109883 0.860 0.749 9.287204e-27
## TUBA1B 2.206990e-22 0.8101951 1.000 0.952 6.166991e-18
## RHOF 1.796442e-18 0.8010583 0.888 0.787 5.019799e-14
## NT5DC3 4.641306e-21 0.8006156 0.561 0.291 1.296920e-16
## YWHAE 4.234606e-31 0.7999126 0.981 0.923 1.183276e-26
## LY6G6F 0.000000e+00 0.7985662 0.813 0.019 0.000000e+00
## PNP 5.964656e-31 0.7981192 0.897 0.784 1.666704e-26
## YIF1B 8.939262e-40 0.7966542 0.935 0.799 2.497898e-35
## GNAZ 6.869875e-189 0.7965159 0.804 0.074 1.919649e-184
## RHEB 5.674030e-37 0.7950470 0.935 0.808 1.585494e-32
## DCLRE1A 2.980292e-64 0.7918369 0.664 0.152 8.327830e-60
## HHEX 9.724722e-10 0.7895102 0.682 0.481 2.717379e-05
## RPA1 6.583654e-33 0.7778641 0.916 0.748 1.839671e-28
## SEMA4D 1.045824e-38 0.7777186 0.981 0.841 2.922347e-34
## UBA7 1.618011e-36 0.7751581 0.916 0.799 4.521209e-32
## STRN4 2.471315e-36 0.7735694 0.832 0.502 6.905595e-32
## RNF10 3.329578e-25 0.7678167 0.916 0.895 9.303841e-21
## TBC1D20 2.054175e-32 0.7676601 0.841 0.663 5.739982e-28
## ACP1 3.796947e-21 0.7673183 0.953 0.935 1.060981e-16
## PNKD 2.275172e-26 0.7656327 0.963 0.921 6.357512e-22
## CDIP1 2.710492e-37 0.7637841 0.869 0.619 7.573927e-33
## LINC02284 0.000000e+00 0.7617681 0.673 0.017 0.000000e+00
## MT-ND2 3.756746e-15 0.7616859 1.000 1.000 1.049748e-10
## TST 2.532150e-42 0.7589511 0.757 0.323 7.075587e-38
## IKBKG 1.734013e-37 0.7578654 0.925 0.797 4.845351e-33
## DIAPH1 4.430656e-18 0.7575282 0.944 0.914 1.238058e-13
## BAMBI 3.188943e-158 0.7570878 0.364 0.015 8.910863e-154
## HGD 1.679127e-307 0.7556406 0.720 0.032 4.691986e-303
## DENND4C 5.314864e-20 0.7555158 0.822 0.758 1.485133e-15
## LINC01011 5.696065e-78 0.7552571 0.822 0.221 1.591651e-73
## PDLIM7 8.393337e-38 0.7548869 0.850 0.487 2.345350e-33
## HIST1H2BN 5.520423e-23 0.7486783 0.664 0.402 1.542572e-18
## PDGFA 9.468382e-09 0.7459432 0.972 0.887 2.645750e-04
## PARK7 1.356814e-19 0.7425930 0.981 0.984 3.791345e-15
## PARD3 0.000000e+00 0.7425616 0.654 0.008 0.000000e+00
## JAM3 2.251687e-93 0.7422465 0.766 0.147 6.291889e-89
## ZCCHC17 5.883981e-35 0.7415737 0.897 0.763 1.644161e-30
## GCLM 1.439365e-33 0.7413876 0.804 0.514 4.022016e-29
## CTNNAL1 8.011008e-92 0.7405076 0.673 0.107 2.238516e-87
## TRAPPC3L 7.997918e-57 0.7371917 0.804 0.300 2.234858e-52
## GATA2 0.000000e+00 0.7371460 0.626 0.012 0.000000e+00
## TSPAN9 0.000000e+00 0.7370231 0.776 0.019 0.000000e+00
## HDGF 3.684407e-28 0.7370092 0.879 0.684 1.029534e-23
## DOK2 1.239255e-13 0.7370075 0.953 0.871 3.462850e-09
## HNRNPLL 1.995473e-20 0.7295451 0.776 0.584 5.575952e-16
## AP2M1 9.756974e-22 0.7276813 1.000 0.972 2.726391e-17
## C5orf30 3.023592e-50 0.7254974 0.645 0.184 8.448823e-46
## MTFR1L 3.556057e-29 0.7251519 0.813 0.647 9.936691e-25
## ABI1 2.402977e-18 0.7238919 0.935 0.923 6.714638e-14
## IFI27L2 2.968469e-14 0.7217705 0.935 0.927 8.294792e-10
## YBX3 2.881471e-11 0.7212505 0.766 0.606 8.051694e-07
## SERPINB1 1.050578e-15 0.7175947 0.963 0.972 2.935630e-11
## PACSIN2 1.671265e-23 0.7151436 0.785 0.656 4.670015e-19
## BTK 3.472288e-30 0.7149630 0.850 0.457 9.702615e-26
## PEAR1 0.000000e+00 0.7146202 0.738 0.008 0.000000e+00
## GNB5 4.481160e-35 0.7112557 0.841 0.571 1.252170e-30
## TOP1 1.195265e-08 0.7094304 0.879 0.911 3.339928e-04
## TGFB1I1 1.794412e-98 0.7086762 0.757 0.136 5.014125e-94
## PELI2 1.055980e-22 0.7073409 0.682 0.393 2.950726e-18
## CTDSPL 2.588227e-148 0.7070436 0.822 0.105 7.232283e-144
## SLC44A1 1.232356e-26 0.7058025 0.822 0.632 3.443571e-22
## RAP1A 2.556568e-08 0.7045030 0.953 0.964 7.143819e-04
## WIPI1 2.369535e-36 0.7019102 0.785 0.439 6.621190e-32
## P2RY1 1.932550e-85 0.7012568 0.720 0.137 5.400125e-81
## PBXIP1 2.583145e-30 0.7001607 0.963 0.854 7.218082e-26
## RGS3 4.046340e-12 0.6967239 0.710 0.610 1.130669e-07
## HEXIM1 6.348199e-22 0.6964872 0.785 0.666 1.773877e-17
## PHKB 1.179552e-27 0.6926173 0.888 0.784 3.296021e-23
## AIG1 7.487786e-44 0.6885595 0.850 0.510 2.092312e-39
## SERPINE1 3.956076e-128 0.6838366 0.617 0.062 1.105446e-123
## CCS 1.975921e-16 0.6837062 0.879 0.880 5.521316e-12
## ARHGDIB 3.681051e-17 0.6829753 1.000 1.000 1.028596e-12
## RHOBTB1 1.616375e-270 0.6791786 0.757 0.042 4.516637e-266
## DGKD 1.774457e-29 0.6780609 0.897 0.785 4.958366e-25
## MOB2 6.817985e-27 0.6753980 0.850 0.781 1.905150e-22
## CABP5 0.000000e+00 0.6749320 0.720 0.018 0.000000e+00
## PROS1 1.075753e-132 0.6723866 0.636 0.064 3.005975e-128
## AC100810.1 6.880715e-33 0.6687461 0.850 0.605 1.922678e-28
## PIP4K2A 3.035192e-29 0.6673676 0.907 0.782 8.481236e-25
## TMEM219 2.758475e-22 0.6633705 0.991 0.963 7.708006e-18
## PHF20L1 2.731335e-22 0.6630029 0.879 0.849 7.632170e-18
## WHAMM 1.615246e-31 0.6580241 0.916 0.842 4.513482e-27
## KIFAP3 9.086431e-23 0.6579471 0.766 0.609 2.539021e-18
## ARL8B 1.611630e-20 0.6576041 0.907 0.844 4.503377e-16
## CPNE5 9.986401e-69 0.6572004 0.813 0.205 2.790500e-64
## HIST1H2AE 2.271400e-43 0.6560102 0.738 0.303 6.346972e-39
## CRAT 2.929121e-41 0.6557072 0.813 0.446 8.184843e-37
## BET1 7.193876e-27 0.6550973 0.813 0.644 2.010185e-22
## SNX9 4.205177e-26 0.6545698 0.794 0.573 1.175053e-21
## MAP4K2 8.937000e-24 0.6513379 0.822 0.726 2.497266e-19
## MCTP1 7.993094e-34 0.6508573 0.860 0.435 2.233510e-29
## FLI1 2.699560e-13 0.6476140 0.925 0.952 7.543380e-09
## MAD2L1BP 1.204828e-13 0.6449215 0.636 0.501 3.366650e-09
## FNBP1L 6.929060e-114 0.6444866 0.626 0.072 1.936187e-109
## ABHD4 6.189023e-47 0.6418044 0.766 0.315 1.729399e-42
## CREG1 2.178266e-21 0.6412710 0.869 0.577 6.086730e-17
## VASP 3.484638e-11 0.6411233 0.972 0.954 9.737125e-07
## RGS6 0.000000e+00 0.6400721 0.776 0.025 0.000000e+00
## ZNF24 4.675560e-09 0.6384470 0.850 0.881 1.306492e-04
## TRIM13 8.605502e-10 0.6381048 0.673 0.623 2.404636e-05
## VTI1B 2.849210e-21 0.6304099 0.879 0.854 7.961546e-17
## ZNF438 1.898685e-53 0.6263315 0.860 0.393 5.305496e-49
## TSC22D3 3.427137e-14 0.6253669 1.000 0.996 9.576448e-10
## WRB 6.547754e-23 0.6240913 0.720 0.512 1.829639e-18
## SERPINE2 6.845056e-62 0.6239627 0.692 0.177 1.912714e-57
## MIGA2 1.862859e-32 0.6236828 0.766 0.467 5.205388e-28
## MITF 4.163206e-58 0.6223749 0.692 0.185 1.163325e-53
## SMPD1 2.662514e-15 0.6216260 0.776 0.680 7.439863e-11
## ARF3 4.139421e-27 0.6198516 0.925 0.833 1.156678e-22
## NDUFA5 6.619627e-26 0.6195196 0.935 0.904 1.849722e-21
## NCK1-DT 9.209999e-36 0.6190517 0.692 0.312 2.573550e-31
## UBE2F 1.171657e-20 0.6190012 0.804 0.688 3.273960e-16
## MTHFD2L 3.475791e-32 0.6174207 0.794 0.561 9.712402e-28
## ALDOA 2.019139e-13 0.6147875 1.000 0.991 5.642081e-09
## ZFAND6 2.291373e-16 0.6135907 0.888 0.878 6.402783e-12
## SRC 4.639475e-48 0.6119558 0.692 0.222 1.296409e-43
## MICU1 1.661385e-22 0.6108221 0.832 0.742 4.642407e-18
## ZFAND3 2.327014e-27 0.6089578 0.850 0.795 6.502375e-23
## ADD3 2.407433e-11 0.6068602 0.981 0.973 6.727091e-07
## FHL2 1.240493e-89 0.6059260 0.701 0.124 3.466309e-85
## PCNP 2.290544e-23 0.6030317 0.972 0.935 6.400466e-19
## MAFG 6.486599e-32 0.6015628 0.720 0.342 1.812550e-27
## ARHGAP21 2.291303e-43 0.6013469 0.804 0.382 6.402588e-39
## EPS15L1 1.350905e-20 0.6001476 0.822 0.736 3.774833e-16
## RASA3 3.613956e-22 0.5946435 0.944 0.857 1.009848e-17
## RMC1 4.584361e-09 0.5945015 0.692 0.689 1.281008e-04
## HPSE 1.053553e-10 0.5942875 0.598 0.434 2.943944e-06
## STXBP2 9.328693e-15 0.5941401 0.953 0.898 2.606717e-10
## STIM1 2.482304e-18 0.5935883 0.850 0.792 6.936303e-14
## PCMT1 2.818386e-16 0.5906931 0.944 0.906 7.875417e-12
## RTN4 8.241746e-10 0.5880746 0.981 0.956 2.302991e-05
## PECAM1 6.038293e-19 0.5870662 0.953 0.775 1.687280e-14
## PRKCD 1.038098e-19 0.5863701 0.879 0.673 2.900757e-15
## EHD1 1.047292e-20 0.5857653 0.925 0.854 2.926449e-16
## TSC22D4 1.473788e-07 0.5829435 0.888 0.946 4.118207e-03
## HSPB1 2.480690e-09 0.5824440 0.888 0.886 6.931792e-05
## ZNF778 8.660972e-25 0.5821404 0.654 0.361 2.420135e-20
## GNA15 5.953846e-32 0.5814142 0.776 0.362 1.663683e-27
## ARHGEF12 1.177078e-21 0.5782002 0.776 0.641 3.289110e-17
## TSPAN33 1.622846e-67 0.5775048 0.748 0.197 4.534719e-63
## BEX4 5.656950e-26 0.5764796 0.860 0.705 1.580722e-21
## RBM38 9.382923e-20 0.5748784 0.757 0.626 2.621870e-15
## PRKAR1A 2.267515e-18 0.5743777 0.925 0.924 6.336116e-14
## AP001033.1 5.369742e-290 0.5740134 0.701 0.032 1.500467e-285
## NUTM2A-AS1 1.043662e-15 0.5737398 0.860 0.837 2.916304e-11
## BRD3 1.298202e-18 0.5714905 0.748 0.678 3.627565e-14
## WDR11-AS1 2.814761e-112 0.5702100 0.645 0.079 7.865287e-108
## N4BP2L1 4.378587e-24 0.5665550 0.907 0.823 1.223509e-19
## ITFG1 2.816338e-22 0.5659464 0.860 0.758 7.869693e-18
## EPB41 1.672223e-13 0.5635762 0.888 0.886 4.672692e-09
## SSFA2 1.688819e-24 0.5632567 0.813 0.557 4.719066e-20
## RIT1 6.011178e-29 0.5615583 0.888 0.732 1.679703e-24
## TENT5C 1.903339e-18 0.5613597 0.776 0.501 5.318500e-14
## H1F0 1.999126e-41 0.5602859 0.776 0.314 5.586159e-37
## SPX 1.745513e-201 0.5585708 0.682 0.047 4.877487e-197
## GNAQ 1.627936e-24 0.5581892 0.869 0.638 4.548942e-20
## STMP1 5.102699e-15 0.5581106 0.897 0.961 1.425847e-10
## MSN 3.050553e-16 0.5560420 0.991 0.990 8.524161e-12
## NENF 5.690325e-16 0.5552799 0.757 0.617 1.590048e-11
## CHMP6 2.615093e-24 0.5550973 0.794 0.687 7.307353e-20
## DCTN2 3.371733e-23 0.5545086 0.935 0.880 9.421635e-19
## GTF3C6 5.869910e-13 0.5537093 0.860 0.896 1.640229e-08
## LANCL3 9.713110e-37 0.5516541 0.701 0.306 2.714134e-32
## TMEM164 3.381189e-29 0.5515335 0.720 0.382 9.448058e-25
## PADI4 4.676777e-30 0.5515043 0.794 0.405 1.306832e-25
## SERF2 7.012891e-08 0.5514500 1.000 1.000 1.959612e-03
## CD82 7.763164e-28 0.5498710 0.935 0.701 2.169261e-23
## XIRP2 0.000000e+00 0.5478272 0.262 0.002 0.000000e+00
## CRYL1 5.229145e-32 0.5476575 0.850 0.645 1.461180e-27
## ORAI2 1.397301e-22 0.5474244 0.841 0.735 3.904478e-18
## TRAPPC1 8.962150e-07 0.5428986 0.981 0.988 2.504294e-02
## MISP3 1.790578e-102 0.5428028 0.692 0.102 5.003411e-98
## MINK1 3.975705e-28 0.5415787 0.832 0.691 1.110931e-23
## RAB11B 6.776442e-24 0.5415297 0.991 0.955 1.893541e-19
## TERF2IP 1.159118e-14 0.5415054 0.963 0.953 3.238924e-10
## PCMTD1 2.972637e-23 0.5408221 0.907 0.838 8.306438e-19
## PHTF2 6.185098e-18 0.5402861 0.813 0.721 1.728302e-13
## KLF3 2.151813e-09 0.5385303 0.879 0.901 6.012810e-05
## HIST1H2BO 4.965153e-85 0.5347372 0.383 0.034 1.387413e-80
## F2RL3 4.578757e-87 0.5339539 0.720 0.135 1.279442e-82
## CDK5RAP2 2.335907e-29 0.5314064 0.813 0.599 6.527226e-25
## ANKRD9 7.215073e-79 0.5293369 0.626 0.108 2.016108e-74
## SCYL2 8.392936e-20 0.5285061 0.785 0.682 2.345238e-15
## USP39 4.574138e-14 0.5272452 0.757 0.737 1.278151e-09
## RHOA 5.393021e-14 0.5259249 1.000 0.999 1.506972e-09
## GDI1 1.255596e-15 0.5255439 0.860 0.861 3.508513e-11
## MXD1 1.527110e-09 0.5248313 0.729 0.694 4.267204e-05
## CALD1 2.299706e-95 0.5244420 0.710 0.119 6.426068e-91
## ITGB5 1.082720e-294 0.5229493 0.701 0.031 3.025444e-290
## EMD 1.245515e-14 0.5225092 0.879 0.889 3.480341e-10
## LINC00211 1.689830e-141 0.5223276 0.729 0.082 4.721892e-137
## VPS28 2.243921e-14 0.5193633 0.972 0.987 6.270190e-10
## SUSD3 7.270351e-19 0.5192844 0.748 0.531 2.031554e-14
## LINC01151 1.540633e-274 0.5187484 0.720 0.036 4.304990e-270
## RHOC 9.225871e-17 0.5177565 0.841 0.632 2.577985e-12
## RABGAP1L 3.940461e-11 0.5172724 0.907 0.930 1.101083e-06
## SF3A1 5.354565e-11 0.5169988 0.869 0.893 1.496226e-06
## AKIRIN1 3.240751e-19 0.5164984 0.879 0.839 9.055629e-15
## ORAI1 4.083298e-20 0.5163613 0.869 0.779 1.140996e-15
## MAP1LC3B 1.128474e-14 0.5143517 0.963 0.937 3.153296e-10
## AMIGO2 1.762993e-26 0.5141721 0.579 0.248 4.926331e-22
## CAPN2 7.717738e-14 0.5141463 0.972 0.932 2.156567e-09
## PLOD2 5.728660e-212 0.5133753 0.654 0.040 1.600759e-207
## EPOR 1.280603e-30 0.5131580 0.682 0.352 3.578390e-26
## HDAC5 4.447202e-24 0.5130541 0.860 0.683 1.242682e-19
## ADI1 7.089057e-16 0.5124758 0.841 0.785 1.980895e-11
## TECPR2 5.844768e-13 0.5119514 0.570 0.418 1.633204e-08
## RYBP 3.012259e-27 0.5114188 0.804 0.529 8.417154e-23
## MORF4L1 9.675523e-09 0.5106555 0.991 0.991 2.703631e-04
## TWSG1 5.142743e-33 0.5077897 0.645 0.271 1.437037e-28
## TMOD3 2.937292e-21 0.5043707 0.925 0.929 8.207675e-17
## GUCY1A1 1.480010e-123 0.5013164 0.692 0.083 4.135592e-119
## AHCTF1 2.398824e-20 0.5010947 0.813 0.708 6.703033e-16
## WASHC1 1.209771e-12 0.4992260 0.972 0.969 3.380462e-08
## SYMPK 1.950157e-24 0.4985817 0.869 0.742 5.449324e-20
## TAOK3 1.564051e-11 0.4950276 0.963 0.952 4.370427e-07
## TM6SF1 3.456106e-51 0.4949792 0.766 0.272 9.657397e-47
## NDUFB4 3.135830e-09 0.4938730 0.981 0.979 8.762451e-05
## SLFN14 0.000000e+00 0.4911333 0.589 0.015 0.000000e+00
## MAPK14 1.866660e-07 0.4907773 0.664 0.646 5.216009e-03
## SRSF6 1.943625e-12 0.4907610 0.850 0.835 5.431071e-08
## RNF115 1.554052e-10 0.4898937 0.785 0.811 4.342487e-06
## ATP9A 2.856098e-142 0.4898907 0.645 0.061 7.980793e-138
## AP001189.3 0.000000e+00 0.4893590 0.561 0.016 0.000000e+00
## ATP2C1 1.329872e-24 0.4893317 0.766 0.555 3.716061e-20
## TMEM185A 3.975468e-30 0.4887431 0.729 0.426 1.110865e-25
## ASB8 4.725083e-15 0.4882883 0.813 0.805 1.320330e-10
## TSC2 5.019292e-16 0.4880102 0.813 0.761 1.402541e-11
## DGKG 2.629631e-56 0.4859739 0.682 0.184 7.347978e-52
## KAT6A 2.167364e-15 0.4831822 0.850 0.879 6.056265e-11
## DLST 5.194039e-07 0.4829342 0.720 0.784 1.451370e-02
## CCPG1 1.330693e-18 0.4813330 0.879 0.805 3.718355e-14
## WFDC1 5.177879e-11 0.4786444 0.355 0.165 1.446855e-06
## NT5M 3.672967e-204 0.4765973 0.701 0.049 1.026337e-199
## VAPB 1.765929e-22 0.4765206 0.748 0.593 4.934534e-18
## ATL1 4.473839e-68 0.4753848 0.701 0.164 1.250125e-63
## CMAS 3.872987e-12 0.4728515 0.692 0.650 1.082229e-07
## GRHL1 6.165863e-109 0.4724076 0.589 0.067 1.722927e-104
## HIST1H2BG 1.270140e-56 0.4692003 0.645 0.160 3.549153e-52
## MSANTD3 4.530545e-42 0.4675706 0.673 0.241 1.265970e-37
## KLF9 2.942684e-10 0.4673185 0.738 0.674 8.222743e-06
## FN3K 1.149433e-35 0.4653946 0.551 0.174 3.211862e-31
## TTLL7 9.407360e-78 0.4651854 0.626 0.108 2.628699e-73
## PANX1 5.770598e-26 0.4637096 0.682 0.384 1.612478e-21
## MPL 0.000000e+00 0.4633860 0.570 0.016 0.000000e+00
## SYTL4 4.173376e-136 0.4633082 0.617 0.058 1.166166e-131
## SELENOT 1.535354e-10 0.4620668 0.953 0.952 4.290239e-06
## LINC01089 4.431013e-29 0.4619490 0.776 0.506 1.238158e-24
## UNC13D 2.681850e-16 0.4600827 0.841 0.798 7.493894e-12
## MTSS1 1.106052e-15 0.4600437 0.897 0.735 3.090642e-11
## YWHAQ 3.119072e-12 0.4596159 0.944 0.953 8.715623e-08
## CFAP161 0.000000e+00 0.4584249 0.523 0.013 0.000000e+00
## KLHL6 3.796417e-20 0.4576029 0.710 0.523 1.060833e-15
## PYGB 3.090293e-17 0.4573558 0.729 0.605 8.635207e-13
## RAB6B 1.915072e-146 0.4558996 0.607 0.051 5.351286e-142
## SEPT4 2.474993e-64 0.4530366 0.626 0.132 6.915873e-60
## SLBP 4.472201e-13 0.4527023 0.869 0.805 1.249667e-08
## LINC00534 0.000000e+00 0.4526788 0.607 0.019 0.000000e+00
## CALM1 2.486752e-11 0.4503843 1.000 0.998 6.948732e-07
## ATF4 1.748276e-13 0.4496265 0.935 0.913 4.885208e-09
## LPAR5 4.317594e-32 0.4493893 0.664 0.280 1.206465e-27
## INAFM2 5.975619e-54 0.4484714 0.720 0.222 1.669767e-49
## LRP12 1.709617e-60 0.4439938 0.561 0.109 4.777182e-56
## TTC33 4.196728e-27 0.4421813 0.701 0.398 1.172692e-22
## RBPMS2 4.336679e-87 0.4404606 0.486 0.054 1.211798e-82
## TOLLIP 4.657754e-19 0.4368302 0.850 0.732 1.301516e-14
## TMEM39B 1.384108e-06 0.4344875 0.570 0.547 3.867613e-02
## TANGO2 5.843599e-19 0.4344491 0.757 0.670 1.632877e-14
## YPEL2 6.170234e-10 0.4326104 0.710 0.662 1.724149e-05
## GOLGA2 1.924407e-09 0.4322203 0.710 0.721 5.377370e-05
## PKIG 3.507997e-47 0.4317437 0.766 0.241 9.802396e-43
## NAP1L4 2.478619e-14 0.4311727 0.907 0.906 6.926005e-10
## TSPOAP1-AS1 5.598423e-36 0.4306452 0.804 0.457 1.564367e-31
## AL157895.1 1.654775e-159 0.4298780 0.654 0.055 4.623937e-155
## LRRFIP2 3.306093e-09 0.4291645 0.804 0.801 9.238215e-05
## MBP 1.122038e-07 0.4265520 0.925 0.942 3.135312e-03
## STX1A 1.981376e-77 0.4264847 0.636 0.112 5.536559e-73
## TVP23B 1.137608e-23 0.4256256 0.822 0.638 3.178818e-19
## CDS2 5.974715e-10 0.4255160 0.841 0.858 1.669515e-05
## RIPOR3 5.832403e-49 0.4254294 0.523 0.114 1.629748e-44
## NRDC 2.406516e-10 0.4250480 0.869 0.911 6.724528e-06
## IGF2BP2 4.993480e-47 0.4249285 0.701 0.233 1.395328e-42
## FUT8 7.771301e-15 0.4237250 0.664 0.522 2.171535e-10
## E2F3 2.224683e-12 0.4220703 0.692 0.599 6.216431e-08
## SLC6A4 5.009157e-97 0.4211098 0.607 0.081 1.399709e-92
## MADD 6.983460e-21 0.4210056 0.804 0.693 1.951388e-16
## TMEM64 3.493763e-34 0.4204625 0.645 0.261 9.762623e-30
## TRAM1 6.848880e-11 0.4197414 0.972 0.951 1.913783e-06
## ERBIN 7.853006e-07 0.4188621 0.832 0.879 2.194365e-02
## PTPRJ 4.967877e-21 0.4186918 0.804 0.638 1.388174e-16
## PDE4D 1.499318e-24 0.4181802 0.832 0.576 4.189544e-20
## HIST1H2BD 1.613068e-38 0.4164209 0.495 0.126 4.507397e-34
## NME4 1.225293e-07 0.4157467 0.710 0.679 3.423835e-03
## VAMP7 9.365839e-21 0.4147915 0.813 0.718 2.617096e-16
## POLR2E 6.848862e-12 0.4144038 0.972 0.958 1.913777e-07
## CRYM 8.545886e-141 0.4132462 0.327 0.013 2.387977e-136
## STARD3NL 6.468860e-16 0.4121325 0.850 0.815 1.807594e-11
## SUCNR1 3.265381e-139 0.4094270 0.364 0.017 9.124453e-135
## UBE2H 2.435841e-23 0.4077934 0.785 0.616 6.806471e-19
## FSTL1 2.234481e-269 0.4073621 0.551 0.020 6.243810e-265
## VIL1 2.695557e-289 0.4072133 0.682 0.030 7.532195e-285
## PVALB 3.318249e-195 0.4050588 0.598 0.036 9.272184e-191
## RALY 7.552070e-10 0.4049533 0.972 0.960 2.110275e-05
## PLA2G4A 3.712855e-13 0.4043761 0.374 0.160 1.037483e-08
## SMTN 6.689039e-24 0.4041456 0.383 0.109 1.869118e-19
## AP002478.1 0.000000e+00 0.4037521 0.636 0.010 0.000000e+00
## MYEOV 1.004821e-196 0.4034890 0.626 0.039 2.807770e-192
## AC008124.1 3.291016e-15 0.4005593 0.682 0.576 9.196087e-11
## PIK3CB 9.071085e-20 0.3999438 0.710 0.532 2.534733e-15
## TNFAIP8L1 2.102402e-11 0.3991390 0.598 0.484 5.874741e-07
## ZFAND2B 1.701454e-09 0.3987641 0.794 0.825 4.754372e-05
## CXCL3 3.292832e-179 0.3973460 0.579 0.036 9.201160e-175
## ACTR10 4.108610e-13 0.3962960 0.850 0.845 1.148069e-08
## CDKL1 8.217929e-25 0.3945210 0.598 0.283 2.296336e-20
## MTRNR2L6 1.899880e-09 0.3945131 0.981 0.966 5.308835e-05
## LYPLAL1-DT 5.761266e-226 0.3939883 0.626 0.033 1.609870e-221
## SCFD2 4.547981e-17 0.3937465 0.654 0.450 1.270842e-12
## ASAP1 4.609338e-15 0.3933598 0.794 0.641 1.287987e-10
## GRK5 1.287829e-25 0.3926103 0.720 0.450 3.598581e-21
## CASP6 3.119056e-19 0.3923767 0.682 0.460 8.715579e-15
## ANK1 1.964501e-88 0.3912175 0.626 0.094 5.489405e-84
## SLC50A1 2.378181e-20 0.3909813 0.869 0.769 6.645352e-16
## SAV1 2.040770e-25 0.3908135 0.654 0.338 5.702523e-21
## DYTN 0.000000e+00 0.3906174 0.467 0.007 0.000000e+00
## BCAP31 9.395317e-14 0.3887732 0.991 0.974 2.625333e-09
## USP12 2.004401e-25 0.3883781 0.794 0.535 5.600897e-21
## TSPAN13 1.306447e-65 0.3855966 0.701 0.134 3.650604e-61
## RAB30 1.369855e-21 0.3855765 0.664 0.352 3.827787e-17
## MPST 2.111006e-17 0.3853382 0.794 0.626 5.898783e-13
## ANXA7 3.208840e-10 0.3842212 0.935 0.922 8.966461e-06
## ADIPOR2 1.296429e-16 0.3838297 0.757 0.657 3.622611e-12
## RSPH9 1.049587e-206 0.3832815 0.589 0.032 2.932862e-202
## SLC9A9 7.079988e-12 0.3824713 0.720 0.627 1.978361e-07
## PRTFDC1 3.461110e-205 0.3803683 0.664 0.043 9.671379e-201
## CTNS 6.209887e-22 0.3801369 0.607 0.325 1.735229e-17
## SUSD1 7.282633e-15 0.3798153 0.626 0.441 2.034986e-10
## C21orf58 3.154249e-36 0.3793532 0.523 0.148 8.813917e-32
## MBTD1 6.423533e-15 0.3783826 0.636 0.468 1.794928e-10
## PXK 4.276029e-15 0.3768468 0.804 0.688 1.194851e-10
## SLC39A3 4.887799e-15 0.3764709 0.607 0.408 1.365798e-10
## DYNLRB1 1.401652e-11 0.3742977 0.981 0.971 3.916636e-07
## RAB13 2.192180e-33 0.3738602 0.748 0.330 6.125608e-29
## DCK 4.139963e-07 0.3733598 0.813 0.812 1.156830e-02
## PTPRA 2.157675e-13 0.3730758 0.813 0.820 6.029192e-09
## METTL22 4.699168e-19 0.3725392 0.748 0.585 1.313088e-14
## SLC9A3R1 1.079106e-09 0.3720858 0.935 0.902 3.015347e-05
## HIST1H2AM 1.523034e-18 0.3689219 0.579 0.309 4.255814e-14
## MAST4 2.782041e-10 0.3678418 0.645 0.550 7.773856e-06
## NEXN-AS1 6.027938e-64 0.3669306 0.477 0.072 1.684387e-59
## SH3TC2 1.246090e-126 0.3668451 0.561 0.050 3.481950e-122
## KLHL5 5.264093e-13 0.3660806 0.626 0.476 1.470945e-08
## PDK1 1.616533e-12 0.3651667 0.692 0.543 4.517079e-08
## STK40 6.664136e-15 0.3649063 0.729 0.598 1.862160e-10
## CALCOCO1 7.382924e-17 0.3634409 0.850 0.798 2.063011e-12
## BX255925.3 1.216530e-52 0.3633898 0.682 0.200 3.399350e-48
## CXCL5 3.313164e-137 0.3626362 0.514 0.038 9.257974e-133
## AATK 7.310068e-46 0.3625503 0.486 0.103 2.042652e-41
## AC074327.1 0.000000e+00 0.3624212 0.654 0.017 0.000000e+00
## AC078883.1 1.248986e-23 0.3611127 0.645 0.332 3.490042e-19
## ASAP2 1.378496e-241 0.3599254 0.533 0.021 3.851932e-237
## GK 1.328316e-21 0.3591681 0.776 0.482 3.711714e-17
## F11R 9.840571e-29 0.3588673 0.720 0.399 2.749751e-24
## VEPH1 0.000000e+00 0.3585106 0.645 0.016 0.000000e+00
## GNA13 8.664363e-13 0.3582465 0.794 0.771 2.421083e-08
## KLHDC8B 1.564195e-37 0.3579727 0.561 0.167 4.370831e-33
## ABTB1 1.489849e-12 0.3575461 0.888 0.894 4.163085e-08
## EGF 0.000000e+00 0.3574526 0.551 0.009 0.000000e+00
## CRKL 3.444854e-11 0.3571606 0.692 0.656 9.625955e-07
## BMP6 0.000000e+00 0.3567522 0.579 0.013 0.000000e+00
## TBC1D15 2.738787e-13 0.3565065 0.692 0.642 7.652994e-09
## EFNB1 1.847356e-34 0.3563204 0.551 0.177 5.162066e-30
## DENND2C 3.204037e-29 0.3560985 0.514 0.178 8.953040e-25
## TCEAL9 1.059924e-92 0.3556975 0.579 0.076 2.961745e-88
## RNF8 4.472907e-07 0.3553314 0.570 0.562 1.249864e-02
## FAM214B 9.206248e-10 0.3550487 0.617 0.484 2.572502e-05
## NEK7 7.822874e-14 0.3525986 0.785 0.719 2.185946e-09
## CAV2 7.733098e-56 0.3506573 0.551 0.114 2.160859e-51
## AFAP1 6.922573e-50 0.3504309 0.598 0.153 1.934375e-45
## CHD9 3.631881e-07 0.3499130 0.822 0.848 1.014857e-02
## KALRN 3.033095e-31 0.3495479 0.449 0.121 8.475377e-27
## SLC35D2 7.563341e-29 0.3467564 0.561 0.213 2.113424e-24
## TMSB4Y 8.945733e-39 0.3465331 0.486 0.122 2.499706e-34
## PDGFRA 1.396984e-150 0.3463762 0.486 0.030 3.903592e-146
## FURIN 8.197950e-16 0.3439925 0.710 0.500 2.290753e-11
## MAVS 4.104338e-12 0.3433564 0.776 0.726 1.146875e-07
## CD36 4.850925e-13 0.3418406 0.813 0.433 1.355494e-08
## RCOR3 1.204345e-07 0.3418039 0.692 0.730 3.365301e-03
## HIPK2 3.683171e-08 0.3413056 0.682 0.639 1.029188e-03
## SEPT7 4.727458e-11 0.3410440 0.981 0.988 1.320994e-06
## ARL15 1.308728e-16 0.3406603 0.607 0.411 3.656980e-12
## HBQ1 2.062942e-177 0.3361169 0.682 0.053 5.764478e-173
## LXN 2.097181e-09 0.3355275 0.430 0.267 5.860153e-05
## SLX4 2.724994e-31 0.3352396 0.626 0.260 7.614450e-27
## LUC7L2 8.330547e-09 0.3351763 0.841 0.901 2.327805e-04
## ACER3 9.599910e-13 0.3331336 0.710 0.563 2.682503e-08
## AL135925.1 2.289739e-10 0.3328562 0.654 0.593 6.398219e-06
## CPEB4 7.833312e-10 0.3321559 0.692 0.570 2.188862e-05
## HYI 2.307480e-37 0.3312859 0.636 0.225 6.447790e-33
## P2RY12 1.849090e-147 0.3308463 0.607 0.051 5.166911e-143
## AGBL5 5.357159e-28 0.3297275 0.589 0.250 1.496951e-23
## ABO 1.349029e-37 0.3296180 0.664 0.244 3.769590e-33
## CYTH2 3.428783e-13 0.3295029 0.682 0.601 9.581049e-09
## MCM6 1.625629e-13 0.3289419 0.570 0.365 4.542496e-09
## GNG8 2.783193e-52 0.3282268 0.439 0.073 7.777076e-48
## E2F1 2.861928e-67 0.3278192 0.402 0.046 7.997084e-63
## LAPTM4B 3.768126e-58 0.3265554 0.505 0.089 1.052927e-53
## PGD 3.560631e-10 0.3257505 0.916 0.829 9.949472e-06
## ST3GAL3 1.504297e-17 0.3257098 0.664 0.459 4.203458e-13
## LINC01003 5.745656e-15 0.3250503 0.607 0.400 1.605509e-10
## SEC14L5 4.822101e-81 0.3250231 0.495 0.062 1.347440e-76
## CXCR2 1.877138e-20 0.3247919 0.626 0.327 5.245286e-16
## EFHC2 1.992389e-87 0.3244868 0.570 0.077 5.567333e-83
## RHD 8.945542e-28 0.3244686 0.607 0.273 2.499653e-23
## ZFYVE21 6.317101e-15 0.3234349 0.607 0.425 1.765188e-10
## GRTP1 2.651681e-108 0.3224434 0.439 0.035 7.409592e-104
## ARMCX6 4.047207e-17 0.3206470 0.785 0.703 1.130911e-12
## ITGA9 7.156724e-224 0.3195388 0.636 0.035 1.999803e-219
## SLC18A2 1.855517e-147 0.3191409 0.542 0.039 5.184871e-143
## CCDC92 3.487244e-24 0.3171414 0.682 0.411 9.744406e-20
## LINC00853 0.000000e+00 0.3167887 0.636 0.018 0.000000e+00
## PYCR2 5.292253e-11 0.3165207 0.692 0.601 1.478814e-06
## DPCD 4.584349e-33 0.3149902 0.542 0.178 1.281005e-28
## SLMAP 2.473770e-13 0.3144319 0.804 0.767 6.912454e-09
## PRELID2 1.734744e-13 0.3129877 0.346 0.137 4.847394e-09
## SAMD14 9.511778e-202 0.3110496 0.449 0.018 2.657876e-197
## VKORC1L1 5.793138e-26 0.3107130 0.645 0.324 1.618777e-21
## TRBV7-4 1.767584e-84 0.3105214 0.523 0.066 4.939161e-80
## STK24 1.349929e-13 0.3104391 0.673 0.560 3.772107e-09
## UBE2J1 1.797904e-11 0.3086957 0.944 0.895 5.023882e-07
## HPGD 1.152022e-14 0.3077926 0.542 0.301 3.219094e-10
## WRNIP1 1.565508e-19 0.3062752 0.682 0.472 4.374499e-15
## AL359644.1 1.868977e-42 0.3039829 0.570 0.158 5.222482e-38
## ATP5S 7.859193e-08 0.3036698 0.738 0.780 2.196094e-03
## CTDSP1 5.008180e-10 0.3024605 0.925 0.881 1.399436e-05
## CHST8 0.000000e+00 0.3020627 0.327 0.002 0.000000e+00
## PCYT1B 9.139206e-55 0.3018480 0.542 0.111 2.553768e-50
## STRADB 4.123553e-09 0.3017211 0.598 0.503 1.152244e-04
## RALB 4.563092e-07 0.3015649 0.729 0.691 1.275065e-02
## HBP1 3.898317e-09 0.3009365 0.785 0.791 1.089307e-04
## KRT8 2.374205e-21 0.2998999 0.505 0.217 6.634242e-17
## LSM1 2.336803e-10 0.2986416 0.832 0.828 6.529727e-06
## HEXIM2 9.844493e-14 0.2958326 0.617 0.469 2.750847e-09
## RTN2 3.361733e-80 0.2951173 0.589 0.091 9.393691e-76
## LY6G5C 1.961521e-23 0.2951071 0.598 0.290 5.481077e-19
## BICD2 6.611587e-09 0.2941515 0.692 0.647 1.847476e-04
## IRX3 4.575663e-288 0.2939118 0.505 0.015 1.278577e-283
## TRAPPC2 5.494396e-12 0.2937109 0.832 0.802 1.535299e-07
## MSRB3 3.747676e-51 0.2934470 0.533 0.113 1.047213e-46
## PRR7 1.575057e-12 0.2931008 0.673 0.517 4.401181e-08
## SNAP29 2.731175e-09 0.2901090 0.757 0.744 7.631722e-05
## OXTR 1.331387e-51 0.2893931 0.523 0.109 3.720296e-47
## DUSP22 9.743353e-09 0.2873889 0.776 0.776 2.722585e-04
## SEPT6 1.228069e-06 0.2873015 0.953 0.957 3.431594e-02
## KCTD10 2.833852e-13 0.2861425 0.636 0.494 7.918632e-09
## CDC14B 1.615566e-25 0.2858438 0.607 0.280 4.514375e-21
## OSBP2 4.235871e-98 0.2851691 0.533 0.059 1.183629e-93
## TRBV7-5 0.000000e+00 0.2845580 0.495 0.006 0.000000e+00
## SMIM27 1.623418e-11 0.2842717 0.841 0.818 4.536318e-07
## DBN1 4.055685e-15 0.2842374 0.598 0.397 1.133280e-10
## TMCC2 2.340586e-218 0.2836780 0.505 0.021 6.540300e-214
## MEA1 1.217272e-07 0.2818125 0.907 0.907 3.401422e-03
## CDK2AP1 1.289106e-22 0.2817899 0.654 0.325 3.602150e-18
## UBASH3B 3.601941e-15 0.2817723 0.645 0.455 1.006490e-10
## AL355073.1 4.370204e-236 0.2817045 0.589 0.027 1.221166e-231
## ZNF367 3.016804e-44 0.2811426 0.467 0.096 8.429855e-40
## CYREN 9.496132e-16 0.2806175 0.692 0.552 2.653504e-11
## IRAK2 3.552773e-14 0.2799555 0.495 0.271 9.927515e-10
## NDST1 5.652388e-25 0.2795770 0.570 0.236 1.579447e-20
## FAM81B 2.916091e-255 0.2788937 0.449 0.013 8.148433e-251
## MGAT4B 2.585343e-19 0.2783417 0.645 0.405 7.224225e-15
## PDE3A 2.019422e-220 0.2767824 0.514 0.022 5.642871e-216
## SGMS1 9.366437e-07 0.2763341 0.598 0.587 2.617264e-02
## MCPH1-AS1 1.942971e-93 0.2761988 0.514 0.057 5.429244e-89
## SPSB1 6.354956e-48 0.2749448 0.458 0.086 1.775765e-43
## ARMC8 2.508998e-09 0.2742655 0.766 0.790 7.010893e-05
## ROCK2 1.904013e-08 0.2742226 0.598 0.528 5.320384e-04
## SKAP2 4.125377e-10 0.2733454 0.888 0.766 1.152754e-05
## FAM177A1 5.553179e-10 0.2729623 0.850 0.848 1.551725e-05
## PCGF5 2.986004e-10 0.2717734 0.907 0.885 8.343790e-06
## PITPNM2 1.293393e-35 0.2706456 0.654 0.257 3.614129e-31
## MDM1 1.841459e-16 0.2698311 0.654 0.459 5.145588e-12
## HIST1H2BF 8.259501e-18 0.2687194 0.477 0.215 2.307952e-13
## CLMN 3.717250e-10 0.2672700 0.570 0.397 1.038711e-05
## CGGBP1 5.226565e-07 0.2669938 0.888 0.909 1.460459e-02
## ADORA2B 3.868995e-51 0.2667574 0.458 0.081 1.081113e-46
## MCF2L 2.808018e-26 0.2652387 0.449 0.141 7.846443e-22
## WASF1 2.829577e-27 0.2652220 0.393 0.101 7.906688e-23
## ZNF792 6.954120e-10 0.2645144 0.374 0.198 1.943190e-05
## TNIK 8.504449e-08 0.2642993 0.720 0.565 2.376398e-03
## LRRC8D 2.476599e-14 0.2622868 0.720 0.586 6.920361e-10
## GTDC1 1.976765e-10 0.2617149 0.636 0.531 5.523674e-06
## HRASLS 0.000000e+00 0.2605060 0.449 0.009 0.000000e+00
## AC147067.1 5.783998e-19 0.2600464 0.645 0.397 1.616223e-14
## LRBA 1.270481e-06 0.2587119 0.757 0.755 3.550104e-02
## UBXN2A 2.163787e-08 0.2564860 0.645 0.670 6.046270e-04
## AFAP1L2 3.806139e-65 0.2559404 0.505 0.079 1.063549e-60
## THEM5 1.084704e-187 0.2525794 0.430 0.018 3.030987e-183
## TMEM106C 9.710117e-12 0.2509314 0.701 0.566 2.713298e-07
## PLXDC2 2.848983e-09 0.2505794 0.766 0.440 7.960913e-05
We retrieved the genes coding for inflammatory cytokines highlighted in the original study.
cytokines_Mega_ori_study <- c("PDGFA","TGFB1","TNFSF4","PF4V1","PF4","PPBP")
cytokines_Mega_ori_study %in% rownames(markersMega)
## [1] TRUE TRUE TRUE TRUE TRUE TRUE
Idents(integrated) <- "majorType"
VlnPlot(integrated, features = cytokines_Mega_ori_study,pt.size = 0.1)
We can compare with the expression of these cytokines in the S-S086-2 sample at the single cell and the metacell level.
Idents(SC.list[["S-S086-2"]]) <- "majorType"
pbmc <- pbmc[,pbmc$majorType %in% levels(SC.list[["S-S086-2"]])]
pbmc$majorType <- factor(pbmc$majorType,levels =levels(SC.list[["S-S086-2"]]))
Idents(pbmc) <- "majorType"
VlnPlot(pbmc, features = cytokines_Mega_ori_study,pt.size = 0.1)
VlnPlot(SC.list[["S-S086-2"]], features = cytokines_Mega_ori_study,pt.size = 0.1)
# sampleList <- str_split_fixed(files,pattern = "/",n=4)[,4]
# sampleList <- str_split_fixed(sampleList,pattern = "_",n=2)[,1]
#
# sampleLowMem <- unique(integrated$sampleID[integrated$Sex == "M" ])
# sum(integrated$size[integrated$sampleID %in% sampleLowMem])
#
# sampleLowMemFileList <- files[sampleList %in% sampleLowMem]
#
# saveRDS(sampleLowMemFileList,"../data/lowMemFileList.rds")
#
# sizeAll <- aggregate(integrated@meta.data[,"size"],
# by = list(sampleID = integrated@meta.data[,"sampleID"]),FUN = sum)
#
# sizeAll[order(sizeAll$x),]
#
# sizeF <- aggregate(integrated@meta.data[integrated$Sex == "F","size"],
# by = list(sampleID = integrated@meta.data[integrated$Sex == "F","sampleID"]),FUN = sum)
#
# largestF <- sizeF$sampleID[which.max(sizeF$x)]
#
# sizeH <- aggregate(integrated@meta.data[integrated$Sex == "M","size"],
# by = list(sampleID = integrated@meta.data[integrated$Sex == "M","sampleID"]),FUN = sum)
#
# largestH <- sizeH$sampleID[which.max(sizeH$x)]